| Description |
1 online resource |
| Series |
SpringerBriefs in computer science |
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SpringerBriefs in computer science.
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| Contents |
Intro; Contents; About the Author; List of Figures; List of Tables; 1 Introduction; 1.1 Motivation; 1.2 Who Should Read This Book and Why?; 1.3 Chapter Overview; 2 Group Decision Making and Consensual Processes; 2.1 Decision Making Under Uncertainty; 2.2 Group Decision Making (GDM) Problems; 2.3 Preference Modeling and Aggregation; 2.4 Consensus Building in GDM; 2.4.1 Overview of Consensus Measures; 2.4.2 Consensus Building Approaches; 2.4.3 A Step-by-Step Example of Consensus Model; 2.5 A Quick Overview of Multi-Criteria Decision Making Methods; 2.5.1 Analytic Hierarchy Process (AHP) |
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2.5.2 Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS)3 Scaling Things Up: Large Group Decision Making (LGDM); 3.1 From Small to Large Decision Groups; 3.2 Limitations and Challenges; 3.3 Summary of Research Trends on LGDM; 3.4 Related Disciplines to LGDM; 3.4.1 Cognitive and Behavioral Science (Psychology); 3.4.2 Management and Social Sciences; 3.4.3 Data Science, Machine Learning and Artificial Intelligence; 4 LGDM Approaches and Models: A Literature Review; 4.1 Considerations and Organization of the Literature Review; 4.2 Subgroup Clustering |
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4.2.1 Early Efforts on Subgroup Clustering in LGDM4.2.2 Clustering Methods for MCLGDM and Complex MCLGDM; 4.2.3 Clustering Large Groups in Emergency and Risk Situations; 4.2.4 Clustering Methods Under Fuzziness; 4.2.5 Other Notable Contributions to Subgroup Clustering in LGDM; 4.3 LGDM Methods; 4.3.1 Methods for Complex MCLGDM; 4.3.2 Aggregations Based on Mutual Assessment Support in LGDM; 4.3.3 LGDM Methods with Fuzzy Membership-Based Opinions; 4.3.4 Estimating Incomplete Assessment and Weight Information in LGDM; 4.3.5 LGDM with Linguistic Distribution Assessments |
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4.3.6 LGDM with Double Hierarchy Hesitant Fuzzy Linguistic Information4.4 Consensus in LGDM; 4.4.1 Semi-supervised Consensus Support Approaches; 4.4.2 Consensus in Emergency LGDM; 4.4.3 Consensus Building Under Social Data and Opinion Dynamics; 4.4.4 Consensus for 2-Rank LGDM Problems; 4.4.5 Consensus on Individual Concerns and Satisfactions; 4.4.6 Consensus and Consistency Under Linguistic Information and Anonymity Preservation; 4.4.7 Consensus with Changeable Subgroups of Participants; 4.4.8 Exploring Classical Consensus Models in LGDM; 4.5 Behavior Modeling and Management |
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4.5.1 Detecting and Penalizing Uncooperative Behaviors in CRPs4.5.2 Managing Minority Opinions and Uncooperative Behaviors; 4.5.3 Self-management and Mutual Evaluation Mechanisms for Behavior Management; 4.5.4 Analyzing Diverse Behavioral Styles; 4.6 Theory and Interdisciplinary Approaches; 5 Implementations and Real-World Applications of LGDM Research; 5.1 Large Group Decision Support Systems; 5.1.1 Social LGDSS; 5.1.2 LaSca; 5.1.3 MENTOR; 5.1.4 Web Tool for Emergency LGDM; 5.1.5 COMAS (COnsensus Multi-Agent System); 5.1.6 Multi-Agent System for Scalable GDM |
| Summary |
This SpringerBrief provides a pioneering, central point of reference for the interested reader in Large Group Decision Making trends such as consensus support, fusion and weighting of relevant decision information, subgroup clustering, behavior management, and implementation of decision support systems, among others. Based on the challenges and difficulties found in classical approaches to handle large decision groups, the principles, families of techniques, and newly related disciplines to Large-Group Decision Making (such as Data Science, Artificial Intelligence, Social Network Analysis, Opinion Dynamics, Behavioral and Cognitive Sciences), are discussed. Real-world applications and future directions of research on this novel topic are likewise highlighted |
| Bibliography |
Includes bibliographical references |
| Notes |
Online resource; title from PDF title page (EBSCO, viewed November 6, 2018) |
| Subject |
Decision making -- Data processing
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Group decision making.
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Artificial intelligence.
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Big data.
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Artificial Intelligence
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artificial intelligence.
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Information architecture.
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Databases.
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Artificial intelligence.
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BUSINESS & ECONOMICS -- Industrial Management.
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BUSINESS & ECONOMICS -- Management.
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BUSINESS & ECONOMICS -- Management Science.
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BUSINESS & ECONOMICS -- Organizational Behavior.
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Artificial intelligence
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Big data
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Decision making -- Data processing
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Group decision making
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| Form |
Electronic book
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| ISBN |
9783030010270 |
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3030010279 |
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